
import numpy as np
import operator
import os
from PIL import Image

def createDataSet():
    group = np.array([[1.0, 1.1], [1.0, 1.0], [0, 0], [0, 0.1]])
    labels = ['A', 'A', 'B', 'B']
    return group, labels


def classify0(inX, dataSet, labels, k):
    dataSetSize = dataSet.shape[0]
    diffMat = np.tile(inX, (dataSetSize, 1)) - dataSet
    sqDiffMat = diffMat ** 2
    sqDistances = sqDiffMat.sum(axis=1)
    distances = sqDistances ** 0.5
    sortedDistIndices = distances.argsort()
    classCount = {}
    for i in range(k):
        voteIlabel = labels[sortedDistIndices[i]]
        classCount[voteIlabel] = classCount.get(voteIlabel, 0) + 1
    # for python 2.x
    # sortedClassCount = sorted(classCount.iteritems(),
    #                          key=operator.itemgetter(1), reverse=True)
    sortedClassCount = sorted(classCount.items(),
                              key=operator.itemgetter(1), reverse=True)

    return sortedClassCount[0][0]


def file2matrix(filename):
    fr = open(filename)
    arrayOLines = fr.readlines()
    numberOfLines = len(arrayOLines)
    returnMat = np.zeros((numberOfLines, 3))
    classLabelVector = []
    index = 0
    for line in arrayOLines:
        line = line.strip()
        listFromLine = line.split('\t')
        returnMat[index, :] = listFromLine[0:3]
        label = listFromLine[-1]
        if label == 'didntLike':
            label = 1
        elif label == 'smallDoses':
            label = 2
        elif label == 'largeDoses':
            label = 3
        classLabelVector.append(int(label))
        index += 1

    return returnMat, classLabelVector


def autoNorm(dataSet):
    minVals = dataSet.min(0)
    maxVals = dataSet.max(0)
    ranges = maxVals - minVals
    normDataSet = np.zeros(np.shape(dataSet))
    m = dataSet.shape[0]
    normDataSet = dataSet - np.tile(minVals, (m, 1))
    normDataSet = normDataSet / np.tile(ranges, (m, 1))
    return normDataSet, ranges, minVals


def datingClassTest():
    hoRatio = 0.10
    datingDataMat, datingLabels = file2matrix('datingTestSet.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    m = normMat.shape[0]
    numTestVecs = int(m * hoRatio)
    errorCount = 0.0
    for i in range(numTestVecs):
        classifierResult = classify0(normMat[i,:], normMat[numTestVecs:m, :], datingLabels[numTestVecs:m], 3)
        print("the classifier came back with: %d, the real answer is: %d" % (classifierResult, datingLabels[i]))
        if (classifierResult != datingLabels[i]): errorCount += 1.0

    print("the total error rate is : %f" % (errorCount/float(numTestVecs)))


def classifyPerson():
    resultList = ['not at all', 'in small doses', 'in large doses']
    percentTats = float(input("percentage of time spent playing video games?"))
    ffMiles = float(input("frequent flier miles earned per year?"))
    iceCream = float(input("liters of ice cream consumed per years?"))
    datingDataMat, datingLabels = file2matrix('datingTestSet2.txt')
    normMat, ranges, minVals = autoNorm(datingDataMat)
    inArr = np.array([ffMiles, percentTats, iceCream])
    classifierResult = classify0((inArr - minVals)/ranges, normMat, datingLabels, 3)
    print("You will probably like this person: ", resultList[classifierResult - 1])


def img2Vector(filename):
    returnVect = np.zeros((1, 1024))
    fr = open(filename)
    for i in range(32):
        lineStr = fr.readline()
        for j in range(32):
            returnVect[0, 32*i+j] = int(lineStr[j])
    return returnVect


def handwritingClassTest(trainingDigitsDir, testDigitsDir):
    hwLabels = []
    trainingFileList = os.listdir(trainingDigitsDir)
    m = len(trainingFileList)
    trainingMat = np.zeros((m, 1024))
    for i in range(m):
        fileNameStr = trainingFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        hwLabels.append(classNumStr)
        trainingMat[i, :] = img2Vector('%s/%s' % (trainingDigitsDir, fileNameStr))

    testFileList = os.listdir(testDigitsDir)
    errorCount = 0.0
    mTest = len(testFileList)
    for i in range(mTest):
        fileNameStr = testFileList[i]
        fileStr = fileNameStr.split('.')[0]
        classNumStr = int(fileStr.split('_')[0])
        vectorUnderTest = img2Vector('%s/%s' % (testDigitsDir, fileNameStr))
        classifierResult = classify0(vectorUnderTest, trainingMat, hwLabels, 3)
        print("the classifier came back with: %d, the real answser is: %d" % (classifierResult, classNumStr))
        if classifierResult != classNumStr: errorCount += 1.0

    print("\nthe total number of errors is: %d" % errorCount)
    print("\nthe total error rate is: %f" % (errorCount/float(mTest)))


# convert font png to text
def convertFntImage(fontImgFile, fontTextFile):
    img = Image.open(fontImgFile)
    img = img.resize((32, 32), Image.ANTIALIAS)
    pixels = img.load()
    # print("pixels: %d, %d" % (img.size[0], img.size[1]))
    txtFile = open(fontTextFile, "w")
    for i in range(img.size[1]):
        for j in range(img.size[0]):
            # print("%d" % pixels[i, j])
            if pixels[j, i] > 127:
                txtFile.write('0')
            else:
                txtFile.write('1')
        txtFile.write('\n')
    txtFile.close()


def genPrintableDigits():
    fontSampleList = os.listdir("Fnt")
    fontSampleList.sort()
    m = len(fontSampleList)
    if not os.path.exists("printTrainingDigits"):
        os.makedirs("printTrainingDigits")
    if not os.path.exists("printTestDigits"):
        os.makedirs("printTestDigits")

    for i in range(m):
        print(fontSampleList[i])
        fontList = os.listdir("Fnt/" + fontSampleList[i])
        fontList.sort()
        n = len(fontList)
        # top 1000 as training data
        for j in range(1000):
            digitStr = "printTrainingDigits/%d_%d.txt" % (i, j)
            convertFntImage("Fnt/" + fontSampleList[i] + "/" + fontList[j], digitStr)
        for k in range(1000, n):
            digitStr = "printTestDigits/%d_%d.txt" % (i, k - 1000)
            convertFntImage("Fnt/" + fontSampleList[i] + "/" + fontList[k], digitStr)